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## # A tibble: 6 × 4
##   Date                Latitude Longitude Magnitude
##   <dttm>                 <dbl>     <dbl>     <dbl>
## 1 1965-01-02 00:00:00    19.2      146.        6  
## 2 1965-01-04 00:00:00     1.86     127.        5.8
## 3 1965-01-05 00:00:00   -20.6     -174.        6.2
## 4 1965-01-08 00:00:00   -59.1      -23.6       5.8
## 5 1965-01-09 00:00:00    11.9      126.        5.8
## 6 1965-01-10 00:00:00   -13.4      167.        6.7

Inspect the DataFrame

Data summary
Name quakes
Number of rows 23412
Number of columns 4
_______________________
Column type frequency:
numeric 3
POSIXct 1
________________________
Group variables None

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
Latitude 0 1 1.68 30.11 -77.08 -18.65 -3.57 26.19 86.0 ▂▆▇▅▁
Longitude 0 1 39.64 125.51 -180.00 -76.35 103.98 145.03 180.0 ▃▂▁▂▇
Magnitude 0 1 5.88 0.42 5.50 5.60 5.70 6.00 9.1 ▇▁▁▁▁

Variable type: POSIXct

skim_variable n_missing complete_rate min max median n_unique
Date 3 1 1965-01-02 2016-12-30 1993-11-30 12398

Geospatial Analysis

Examine temporal patterns

Plotting the number of earthquakes by year indicates that earthquakes are becoming more frequent:

This is maybe unexpected. To look into the data more, we can also plot by time and magnitude. This shows that the number of large magnitude events stays relatively constant through time, and the apparent increase entirely comes from smaller magnitude events. This might suggest a detection bias.